mark4

Documentation

Index

• Constants
• func Mark4()
• func Mark4a()
• type Net
  • func NewNet(seed int64, inputs, outputs int) Net
  • func (n Net) CalculateStatistics(systems []Sample) Set
  • func (n *Net) Fire(input Matrix) (float32, Matrix)
• type Random
• type Sample
• type Set
  • func NewStatistics(inputs, outputs int) Set
  • func (s Set) Sample(rng *rand.Rand, inputs, outputs int) []Matrix

Constants

const (
	// Window is the window size
	Window = 8
	// Samples is the number of samples
	Samples = 256
	// Size is the size of the filter
	Size = 64
	// Inputs is the number of inputs
	Inputs = Size
	// Outputs is the number of outputs
	Outputs = 8
)

Functions

func Mark4

func Mark4()

Mark4 is the mark4 model

func Mark4a

func Mark4a()

Mark4a is the mark4a model

Types

type Net

type Net struct {
	Inputs  int
	Outputs int
	Rng     *rand.Rand
	Q       Set
	K       Set
	V       Set
}

Net is a net

func NewNet

func NewNet(seed int64, inputs, outputs int) Net

NewNet makes a new network

func (Net) CalculateStatistics

func (n Net) CalculateStatistics(systems []Sample) Set

CalculateStatistics calculates the statistics of systems

func (*Net) Fire

func (n *Net) Fire(input Matrix) (float32, Matrix)

Fire runs the network

type Random

type Random struct {
	Mean   float32
	StdDev float32
}

Random is a random variable

type Sample

type Sample struct {
	Entropy float32
	Neurons []Matrix
	Outputs Matrix
	Out     Matrix
}

Sample is a sample of a random neural network

type Set

type Set [][]Random

Set is a set of statistics

func NewStatistics

func NewStatistics(inputs, outputs int) Set

NewStatistics generates a new statistics model

func (Set) Sample

func (s Set) Sample(rng *rand.Rand, inputs, outputs int) []Matrix

Sample samples from the statistics